Brenke Ryan, Kozakov Dima, Chuang Gwo-Yu, Beglov Dmitri, Hall David, Landon Melissa R, Mattos Carla, Vajda Sandor
Program in Bioinformatics, Boston University, Boston, MA, USA.
Bioinformatics. 2009 Mar 1;25(5):621-7. doi: 10.1093/bioinformatics/btp036. Epub 2009 Jan 28.
The binding sites of proteins generally contain smaller regions that provide major contributions to the binding free energy and hence are the prime targets in drug design. Screening libraries of fragment-sized compounds by NMR or X-ray crystallography demonstrates that such 'hot spot' regions bind a large variety of small organic molecules, and that a relatively high 'hit rate' is predictive of target sites that are likely to bind drug-like ligands with high affinity. Our goal is to determine the 'hot spots' computationally rather than experimentally.
We have developed the FTMAP algorithm that performs global search of the entire protein surface for regions that bind a number of small organic probe molecules. The search is based on the extremely efficient fast Fourier transform (FFT) correlation approach which can sample billions of probe positions on dense translational and rotational grids, but can use only sums of correlation functions for scoring and hence is generally restricted to very simple energy expressions. The novelty of FTMAP is that we were able to incorporate and represent on grids a detailed energy expression, resulting in a very accurate identification of low-energy probe clusters. Overlapping clusters of different probes are defined as consensus sites (CSs). We show that the largest CS is generally located at the most important subsite of the protein binding site, and the nearby smaller CSs identify other important subsites. Mapping results are presented for elastase whose structure has been solved in aqueous solutions of eight organic solvents, and we show that FTMAP provides very similar information. The second application is to renin, a long-standing pharmaceutical target for the treatment of hypertension, and we show that the major CSs trace out the shape of the first approved renin inhibitor, aliskiren.
FTMAP is available as a server at http://ftmap.bu.edu/.
蛋白质的结合位点通常包含对结合自由能有主要贡献的较小区域,因此是药物设计的主要靶点。通过核磁共振(NMR)或X射线晶体学筛选片段大小化合物的文库表明,此类“热点”区域能结合多种小有机分子,且相对较高的“命中率”预示着可能与类药物配体高亲和力结合的靶点位点。我们的目标是通过计算而非实验来确定“热点”。
我们开发了FTMAP算法,该算法对整个蛋白质表面进行全局搜索,以寻找能结合多个小有机探针分子的区域。搜索基于极其高效的快速傅里叶变换(FFT)相关方法,该方法可以在密集的平移和旋转网格上对数十亿个探针位置进行采样,但只能使用相关函数的总和进行评分,因此通常限于非常简单的能量表达式。FTMAP的新颖之处在于我们能够在网格上纳入并表示详细的能量表达式,从而非常准确地识别低能量探针簇。不同探针的重叠簇被定义为共有位点(CSs)。我们表明,最大的CS通常位于蛋白质结合位点最重要的亚位点,附近较小的CSs则识别其他重要亚位点。展示了弹性蛋白酶在八种有机溶剂水溶液中结构已解析情况下的映射结果,我们表明FTMAP提供了非常相似的信息。第二个应用是肾素,它是治疗高血压的一个长期药物靶点,我们表明主要的CSs勾勒出了首个获批的肾素抑制剂阿利吉仑的形状。
FTMAP可作为服务器在http://ftmap.bu.edu/获取。